import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:
     class SCREAMING_SNAKE_CASE_		:







        @staticmethod
        def 						lowerCamelCase_	(     *lowerCamelCase_						:						List[Any]   ,   **lowerCamelCase_						:						Tuple				):





         """simple docstring"""


         pass





def 			lowercase(	UpperCamelCase_       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      hashlib.mda(image.tobytes()       )
 return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):
   __lowerCAmelCase           =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      DepthEstimationPipeline(model=lowerCamelCase_   ,   image_processor=lowerCamelCase_				)
    return depth_estimator, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
    self.assertEqual({"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)}   ,   lowerCamelCase_				)
    import datasets

    UpperCamelCase			  =      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,   """image"""   ,   split="""test"""				)
    UpperCamelCase			  =      depth_estimator(
        [
            Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				),
            """http://images.cocodataset.org/val2017/000000039769.jpg""",
            # RGBA
            dataset[0]["""file"""],
            # LA
            dataset[1]["""file"""],
            # L
            dataset[2]["""file"""],
        ]				)
    self.assertEqual(
        [
            {"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)},
            {"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)},
            {"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)},
            {"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)},
            {"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)},
        ]   ,   lowerCamelCase_   ,   )







   @require_tf
   @unittest.skip("""Depth estimation is not implemented in TF"""				)
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    pass







   @slow
   @require_torch
   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      """Intel/dpt-large"""
    UpperCamelCase			  =      pipeline("""depth-estimation"""   ,   model=lowerCamelCase_				)
    UpperCamelCase			  =      depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
    UpperCamelCase			  =      hashimage(outputs["""depth"""]				)

    # This seems flaky.
    # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
    self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()				)   ,   2_9.3_0_4				)
    self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()				)   ,   2.6_6_2				)







   @require_torch
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""				)

from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      text, pattern
    UpperCamelCase  ,     UpperCamelCase			  =      len(lowerCamelCase_				), len(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if char == self.pattern[i]:
      return i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    for i in range(self.patLen - 1   ,   -1   ,   -1				):
     if self.pattern[i] != self.text[current_pos + i]:
      return current_pos + i
    return -1







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for i in range(self.textLen - self.patLen + 1				):
     UpperCamelCase			  =      self.mismatch_in_text(lowerCamelCase_				)
     if mismatch_index == -1:
      positions.append(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      self.match_in_pattern(self.text[mismatch_index]				)
      UpperCamelCase			  =      (
          mismatch_index - match_index
      )  # shifting index lgtm [py/multiple-definition]
    return positions





_SCREAMING_SNAKE_CASE 		=      """ABAABA"""
_SCREAMING_SNAKE_CASE 		=      """AB"""
_SCREAMING_SNAKE_CASE 		=      BoyerMooreSearch(text, pattern)
_SCREAMING_SNAKE_CASE 		=      bms.bad_character_heuristic()

if len(positions) == 0:
     print("""No match found""")
else:
     print("""Pattern found in following positions: """)
     print(positions)
